Overview

Dataset statistics

Number of variables49
Number of observations49844
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.6 MiB
Average record size in memory392.0 B

Variable types

Numeric8
Categorical41

Alerts

numscreens is highly overall correlated with OtherScreensHigh correlation
OtherScreens is highly overall correlated with numscreens and 2 other fieldsHigh correlation
SavingsScreens is highly overall correlated with VerifyHousing and 5 other fieldsHigh correlation
location is highly overall correlated with VerifyDateOfBirthHigh correlation
VerifyPhone is highly overall correlated with OtherScreens and 1 other fieldsHigh correlation
VerifyDateOfBirth is highly overall correlated with locationHigh correlation
ProfilePage is highly overall correlated with EditProfile and 1 other fieldsHigh correlation
VerifyCountry is highly overall correlated with OtherScreens and 1 other fieldsHigh correlation
EditProfile is highly overall correlated with ProfilePageHigh correlation
VerifyHousing is highly overall correlated with SavingsScreens and 5 other fieldsHigh correlation
VerifyHousingAmount is highly overall correlated with SavingsScreens and 5 other fieldsHigh correlation
ProfileMaritalStatus is highly overall correlated with SavingsScreens and 5 other fieldsHigh correlation
ProfileEducation is highly overall correlated with SavingsScreens and 5 other fieldsHigh correlation
ProfileEducationMajor is highly overall correlated with SavingsScreens and 4 other fieldsHigh correlation
VerifyAnnualIncome is highly overall correlated with SavingsScreens and 3 other fieldsHigh correlation
VerifyIncomeType is highly overall correlated with ProfileEducation and 3 other fieldsHigh correlation
ProfileJobTitle is highly overall correlated with VerifyIncomeType and 1 other fieldsHigh correlation
ProfileEmploymentLength is highly overall correlated with VerifyIncomeType and 1 other fieldsHigh correlation
minigame is highly imbalanced (50.7%)Imbalance
RewardsContainer is highly imbalanced (58.8%)Imbalance
EditProfile is highly imbalanced (72.4%)Imbalance
Finances is highly imbalanced (61.1%)Imbalance
Alerts is highly imbalanced (62.5%)Imbalance
Leaderboard is highly imbalanced (70.8%)Imbalance
VerifyMobile is highly imbalanced (70.0%)Imbalance
VerifyHousing is highly imbalanced (73.9%)Imbalance
RewardDetail is highly imbalanced (80.4%)Imbalance
VerifyHousingAmount is highly imbalanced (75.5%)Imbalance
ProfileMaritalStatus is highly imbalanced (73.2%)Imbalance
ProfileEducation is highly imbalanced (74.0%)Imbalance
ProfileEducationMajor is highly imbalanced (76.0%)Imbalance
Rewards is highly imbalanced (83.5%)Imbalance
AccountView is highly imbalanced (82.7%)Imbalance
VerifyAnnualIncome is highly imbalanced (84.1%)Imbalance
VerifyIncomeType is highly imbalanced (80.2%)Imbalance
ProfileJobTitle is highly imbalanced (84.9%)Imbalance
Login is highly imbalanced (80.8%)Imbalance
ProfileEmploymentLength is highly imbalanced (85.6%)Imbalance
WebView is highly imbalanced (54.5%)Imbalance
SecurityModal is highly imbalanced (89.3%)Imbalance
ResendToken is highly imbalanced (89.8%)Imbalance
TransactionList is highly imbalanced (90.0%)Imbalance
NetworkFailure is highly imbalanced (93.2%)Imbalance
ListPicker is highly imbalanced (93.7%)Imbalance
CreditCardScreens is highly imbalanced (71.4%)Imbalance
dayofweek has 7495 (15.0%) zerosZeros
hour has 2654 (5.3%) zerosZeros
OtherScreens has 774 (1.6%) zerosZeros
CreditMonitoringScreens has 26174 (52.5%) zerosZeros
SavingsScreens has 45689 (91.7%) zerosZeros

Reproduction

Analysis started2023-11-11 03:09:33.527660
Analysis finished2023-11-11 03:09:56.241333
Duration22.71 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

user
Real number (ℝ)

Distinct49733
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186897.64
Minimum13
Maximum373662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:09:56.433887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18865.65
Q193526.75
median187183.5
Q3279996
95-th percentile354704.35
Maximum373662
Range373649
Interquartile range (IQR)186469.25

Descriptive statistics

Standard deviation107772.52
Coefficient of variation (CV)0.57663927
Kurtosis-1.1986762
Mean186897.64
Median Absolute Deviation (MAD)93248
Skewness-0.00060372804
Sum9.3157258 × 109
Variance1.1614915 × 1010
MonotonicityNot monotonic
2023-11-10T22:09:56.653977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
257669 2
 
< 0.1%
159899 2
 
< 0.1%
293752 2
 
< 0.1%
217440 2
 
< 0.1%
336864 2
 
< 0.1%
122942 2
 
< 0.1%
8767 2
 
< 0.1%
14494 2
 
< 0.1%
147309 2
 
< 0.1%
17527 2
 
< 0.1%
Other values (49723) 49824
> 99.9%
ValueCountFrequency (%)
13 1
< 0.1%
23 1
< 0.1%
29 1
< 0.1%
48 1
< 0.1%
53 1
< 0.1%
76 1
< 0.1%
85 1
< 0.1%
95 1
< 0.1%
96 1
< 0.1%
98 1
< 0.1%
ValueCountFrequency (%)
373662 1
< 0.1%
373639 1
< 0.1%
373633 1
< 0.1%
373628 1
< 0.1%
373624 1
< 0.1%
373622 1
< 0.1%
373620 1
< 0.1%
373618 1
< 0.1%
373612 1
< 0.1%
373607 1
< 0.1%

dayofweek
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0287296
Minimum0
Maximum6
Zeros7495
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:09:56.827587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0320193
Coefficient of variation (CV)0.67091473
Kurtosis-1.2903246
Mean3.0287296
Median Absolute Deviation (MAD)2
Skewness-0.041032987
Sum150964
Variance4.1291026
MonotonicityNot monotonic
2023-11-10T22:09:56.958915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 7501
15.0%
0 7495
15.0%
5 7393
14.8%
6 7392
14.8%
1 7123
14.3%
3 6640
13.3%
2 6300
12.6%
ValueCountFrequency (%)
0 7495
15.0%
1 7123
14.3%
2 6300
12.6%
3 6640
13.3%
4 7501
15.0%
5 7393
14.8%
6 7392
14.8%
ValueCountFrequency (%)
6 7392
14.8%
5 7393
14.8%
4 7501
15.0%
3 6640
13.3%
2 6300
12.6%
1 7123
14.3%
0 7495
15.0%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.559445
Minimum0
Maximum23
Zeros2654
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:09:57.101201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median14
Q319
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.4409267
Coefficient of variation (CV)0.59245663
Kurtosis-1.2857841
Mean12.559445
Median Absolute Deviation (MAD)6
Skewness-0.32882778
Sum626013
Variance55.367391
MonotonicityNot monotonic
2023-11-10T22:09:57.244412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 2932
 
5.9%
20 2813
 
5.6%
17 2807
 
5.6%
16 2784
 
5.6%
21 2760
 
5.5%
18 2725
 
5.5%
19 2706
 
5.4%
22 2699
 
5.4%
0 2654
 
5.3%
23 2634
 
5.3%
Other values (14) 22330
44.8%
ValueCountFrequency (%)
0 2654
5.3%
1 2431
4.9%
2 2496
5.0%
3 2155
4.3%
4 1931
3.9%
5 1562
3.1%
6 1281
2.6%
7 1107
2.2%
8 895
 
1.8%
9 767
 
1.5%
ValueCountFrequency (%)
23 2634
5.3%
22 2699
5.4%
21 2760
5.5%
20 2813
5.6%
19 2706
5.4%
18 2725
5.5%
17 2807
5.6%
16 2784
5.6%
15 2932
5.9%
14 2303
4.6%

age
Real number (ℝ)

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.732425
Minimum16
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:09:57.404684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile19
Q124
median29
Q337
95-th percentile53
Maximum101
Range85
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.806681
Coefficient of variation (CV)0.34055641
Kurtosis1.2667859
Mean31.732425
Median Absolute Deviation (MAD)6
Skewness1.119299
Sum1581671
Variance116.78435
MonotonicityNot monotonic
2023-11-10T22:09:57.861950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 2341
 
4.7%
25 2330
 
4.7%
26 2293
 
4.6%
24 2288
 
4.6%
27 2213
 
4.4%
22 2208
 
4.4%
28 2160
 
4.3%
21 2119
 
4.3%
29 2016
 
4.0%
20 1856
 
3.7%
Other values (68) 28020
56.2%
ValueCountFrequency (%)
16 191
 
0.4%
17 694
 
1.4%
18 1193
2.4%
19 1641
3.3%
20 1856
3.7%
21 2119
4.3%
22 2208
4.4%
23 2341
4.7%
24 2288
4.6%
25 2330
4.7%
ValueCountFrequency (%)
101 1
 
< 0.1%
100 2
 
< 0.1%
98 1
 
< 0.1%
90 3
< 0.1%
89 2
 
< 0.1%
88 1
 
< 0.1%
87 5
< 0.1%
86 3
< 0.1%
85 3
< 0.1%
84 3
< 0.1%

numscreens
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.824914
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:09:58.022430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median18
Q328
95-th percentile50
Maximum91
Range90
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.778043
Coefficient of variation (CV)0.70963288
Kurtosis1.9734531
Mean20.824914
Median Absolute Deviation (MAD)9
Skewness1.2873935
Sum1037997
Variance218.39057
MonotonicityNot monotonic
2023-11-10T22:09:58.182790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 1685
 
3.4%
16 1680
 
3.4%
10 1677
 
3.4%
14 1650
 
3.3%
12 1648
 
3.3%
13 1621
 
3.3%
17 1601
 
3.2%
7 1576
 
3.2%
18 1571
 
3.2%
8 1570
 
3.1%
Other values (81) 33565
67.3%
ValueCountFrequency (%)
1 897
1.8%
2 854
1.7%
3 1050
2.1%
4 1307
2.6%
5 1309
2.6%
6 1478
3.0%
7 1576
3.2%
8 1570
3.1%
9 1568
3.1%
10 1677
3.4%
ValueCountFrequency (%)
91 10
< 0.1%
90 11
< 0.1%
89 8
< 0.1%
88 12
< 0.1%
87 11
< 0.1%
86 17
< 0.1%
85 15
< 0.1%
84 8
< 0.1%
83 18
< 0.1%
82 12
< 0.1%

minigame
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
44473 
1
5371 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44473
89.2%
1 5371
 
10.8%

Length

2023-11-10T22:09:58.337700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:09:58.472140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 44473
89.2%
1 5371
 
10.8%

Most occurring characters

ValueCountFrequency (%)
0 44473
89.2%
1 5371
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44473
89.2%
1 5371
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44473
89.2%
1 5371
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44473
89.2%
1 5371
 
10.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
41269 
1
8575 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 41269
82.8%
1 8575
 
17.2%

Length

2023-11-10T22:09:58.609872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:09:58.719448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 41269
82.8%
1 8575
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 41269
82.8%
1 8575
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41269
82.8%
1 8575
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41269
82.8%
1 8575
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41269
82.8%
1 8575
 
17.2%

enrolled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
1
25247 
0
24597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 25247
50.7%
0 24597
49.3%

Length

2023-11-10T22:09:58.837637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:09:58.946781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 25247
50.7%
0 24597
49.3%

Most occurring characters

ValueCountFrequency (%)
1 25247
50.7%
0 24597
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25247
50.7%
0 24597
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25247
50.7%
0 24597
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25247
50.7%
0 24597
49.3%

liked
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
41617 
1
8227 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 41617
83.5%
1 8227
 
16.5%

Length

2023-11-10T22:09:59.066883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:09:59.175713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 41617
83.5%
1 8227
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 41617
83.5%
1 8227
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41617
83.5%
1 8227
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41617
83.5%
1 8227
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41617
83.5%
1 8227
 
16.5%

location
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
1
25768 
0
24076 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 25768
51.7%
0 24076
48.3%

Length

2023-11-10T22:09:59.295791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:09:59.407169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 25768
51.7%
0 24076
48.3%

Most occurring characters

ValueCountFrequency (%)
1 25768
51.7%
0 24076
48.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 25768
51.7%
0 24076
48.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 25768
51.7%
0 24076
48.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 25768
51.7%
0 24076
48.3%

Institutions
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
35257 
1
14587 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 35257
70.7%
1 14587
29.3%

Length

2023-11-10T22:09:59.534072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:09:59.665097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 35257
70.7%
1 14587
29.3%

Most occurring characters

ValueCountFrequency (%)
0 35257
70.7%
1 14587
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35257
70.7%
1 14587
29.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35257
70.7%
1 14587
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35257
70.7%
1 14587
29.3%

VerifyPhone
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
1
26125 
0
23719 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 26125
52.4%
0 23719
47.6%

Length

2023-11-10T22:09:59.819363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:09:59.943821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 26125
52.4%
0 23719
47.6%

Most occurring characters

ValueCountFrequency (%)
1 26125
52.4%
0 23719
47.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26125
52.4%
0 23719
47.6%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26125
52.4%
0 23719
47.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26125
52.4%
0 23719
47.6%

BankVerification
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
33957 
1
15887 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 33957
68.1%
1 15887
31.9%

Length

2023-11-10T22:10:00.065598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:00.176213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 33957
68.1%
1 15887
31.9%

Most occurring characters

ValueCountFrequency (%)
0 33957
68.1%
1 15887
31.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33957
68.1%
1 15887
31.9%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33957
68.1%
1 15887
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33957
68.1%
1 15887
31.9%

VerifyDateOfBirth
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
26282 
1
23562 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 26282
52.7%
1 23562
47.3%

Length

2023-11-10T22:10:00.294082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:00.418346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 26282
52.7%
1 23562
47.3%

Most occurring characters

ValueCountFrequency (%)
0 26282
52.7%
1 23562
47.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26282
52.7%
1 23562
47.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26282
52.7%
1 23562
47.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26282
52.7%
1 23562
47.3%

ProfilePage
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
42070 
1
7774 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 42070
84.4%
1 7774
 
15.6%

Length

2023-11-10T22:10:00.540177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:00.672829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 42070
84.4%
1 7774
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 42070
84.4%
1 7774
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42070
84.4%
1 7774
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42070
84.4%
1 7774
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42070
84.4%
1 7774
 
15.6%

VerifyCountry
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
28778 
1
21066 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 28778
57.7%
1 21066
42.3%

Length

2023-11-10T22:10:00.809125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:00.924975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 28778
57.7%
1 21066
42.3%

Most occurring characters

ValueCountFrequency (%)
0 28778
57.7%
1 21066
42.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28778
57.7%
1 21066
42.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28778
57.7%
1 21066
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28778
57.7%
1 21066
42.3%

Cycle
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
31673 
1
18171 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 31673
63.5%
1 18171
36.5%

Length

2023-11-10T22:10:01.045086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:01.182326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 31673
63.5%
1 18171
36.5%

Most occurring characters

ValueCountFrequency (%)
0 31673
63.5%
1 18171
36.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31673
63.5%
1 18171
36.5%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31673
63.5%
1 18171
36.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31673
63.5%
1 18171
36.5%

idscreen
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
32869 
1
16975 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 32869
65.9%
1 16975
34.1%

Length

2023-11-10T22:10:01.340113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:01.462805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 32869
65.9%
1 16975
34.1%

Most occurring characters

ValueCountFrequency (%)
0 32869
65.9%
1 16975
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32869
65.9%
1 16975
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32869
65.9%
1 16975
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32869
65.9%
1 16975
34.1%

Splash
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
39848 
1
9996 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39848
79.9%
1 9996
 
20.1%

Length

2023-11-10T22:10:01.583226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:01.692584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 39848
79.9%
1 9996
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 39848
79.9%
1 9996
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39848
79.9%
1 9996
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39848
79.9%
1 9996
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39848
79.9%
1 9996
 
20.1%

RewardsContainer
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
45714 
1
 
4130

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45714
91.7%
1 4130
 
8.3%

Length

2023-11-10T22:10:01.811692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:01.920025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 45714
91.7%
1 4130
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 45714
91.7%
1 4130
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45714
91.7%
1 4130
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45714
91.7%
1 4130
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45714
91.7%
1 4130
 
8.3%

EditProfile
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47477 
1
 
2367

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47477
95.3%
1 2367
 
4.7%

Length

2023-11-10T22:10:02.040607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:02.148446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47477
95.3%
1 2367
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 47477
95.3%
1 2367
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47477
95.3%
1 2367
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47477
95.3%
1 2367
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47477
95.3%
1 2367
 
4.7%

Finances
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
46039 
1
 
3805

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 46039
92.4%
1 3805
 
7.6%

Length

2023-11-10T22:10:02.265284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:02.375201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 46039
92.4%
1 3805
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 46039
92.4%
1 3805
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46039
92.4%
1 3805
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46039
92.4%
1 3805
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46039
92.4%
1 3805
 
7.6%

Alerts
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
46230 
1
 
3614

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46230
92.7%
1 3614
 
7.3%

Length

2023-11-10T22:10:02.497339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:02.605361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 46230
92.7%
1 3614
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 46230
92.7%
1 3614
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46230
92.7%
1 3614
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46230
92.7%
1 3614
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46230
92.7%
1 3614
 
7.3%

Leaderboard
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47291 
1
 
2553

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 47291
94.9%
1 2553
 
5.1%

Length

2023-11-10T22:10:02.719120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:02.829504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47291
94.9%
1 2553
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 47291
94.9%
1 2553
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47291
94.9%
1 2553
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47291
94.9%
1 2553
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47291
94.9%
1 2553
 
5.1%

VerifyMobile
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47192 
1
 
2652

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47192
94.7%
1 2652
 
5.3%

Length

2023-11-10T22:10:02.943415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:03.052970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47192
94.7%
1 2652
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 47192
94.7%
1 2652
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47192
94.7%
1 2652
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47192
94.7%
1 2652
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47192
94.7%
1 2652
 
5.3%

VerifyHousing
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47642 
1
 
2202

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47642
95.6%
1 2202
 
4.4%

Length

2023-11-10T22:10:03.168319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:03.277688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47642
95.6%
1 2202
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 47642
95.6%
1 2202
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47642
95.6%
1 2202
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47642
95.6%
1 2202
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47642
95.6%
1 2202
 
4.4%

RewardDetail
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48333 
1
 
1511

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 48333
97.0%
1 1511
 
3.0%

Length

2023-11-10T22:10:03.392297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:03.501590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48333
97.0%
1 1511
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 48333
97.0%
1 1511
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48333
97.0%
1 1511
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48333
97.0%
1 1511
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48333
97.0%
1 1511
 
3.0%

VerifyHousingAmount
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47823 
1
 
2021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47823
95.9%
1 2021
 
4.1%

Length

2023-11-10T22:10:03.617419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:03.725363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47823
95.9%
1 2021
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 47823
95.9%
1 2021
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47823
95.9%
1 2021
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47823
95.9%
1 2021
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47823
95.9%
1 2021
 
4.1%

ProfileMaritalStatus
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47559 
1
 
2285

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47559
95.4%
1 2285
 
4.6%

Length

2023-11-10T22:10:03.841491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:03.954581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47559
95.4%
1 2285
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 47559
95.4%
1 2285
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47559
95.4%
1 2285
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47559
95.4%
1 2285
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47559
95.4%
1 2285
 
4.6%

ProfileEducation
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47654 
1
 
2190

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47654
95.6%
1 2190
 
4.4%

Length

2023-11-10T22:10:04.076311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:04.184320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47654
95.6%
1 2190
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 47654
95.6%
1 2190
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47654
95.6%
1 2190
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47654
95.6%
1 2190
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47654
95.6%
1 2190
 
4.4%

ProfileEducationMajor
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
47875 
1
 
1969

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 47875
96.0%
1 1969
 
4.0%

Length

2023-11-10T22:10:04.300945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:04.409675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 47875
96.0%
1 1969
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 47875
96.0%
1 1969
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47875
96.0%
1 1969
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47875
96.0%
1 1969
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47875
96.0%
1 1969
 
4.0%

Rewards
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48634 
1
 
1210

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 48634
97.6%
1 1210
 
2.4%

Length

2023-11-10T22:10:04.524851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:04.660493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48634
97.6%
1 1210
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 48634
97.6%
1 1210
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48634
97.6%
1 1210
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48634
97.6%
1 1210
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48634
97.6%
1 1210
 
2.4%

AccountView
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48559 
1
 
1285

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48559
97.4%
1 1285
 
2.6%

Length

2023-11-10T22:10:04.792532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:04.908902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48559
97.4%
1 1285
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 48559
97.4%
1 1285
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48559
97.4%
1 1285
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48559
97.4%
1 1285
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48559
97.4%
1 1285
 
2.6%

VerifyAnnualIncome
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48684 
1
 
1160

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48684
97.7%
1 1160
 
2.3%

Length

2023-11-10T22:10:05.026555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:05.134031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48684
97.7%
1 1160
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 48684
97.7%
1 1160
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48684
97.7%
1 1160
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48684
97.7%
1 1160
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48684
97.7%
1 1160
 
2.3%

VerifyIncomeType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48309 
1
 
1535

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48309
96.9%
1 1535
 
3.1%

Length

2023-11-10T22:10:05.251334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:05.359778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48309
96.9%
1 1535
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 48309
96.9%
1 1535
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48309
96.9%
1 1535
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48309
96.9%
1 1535
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48309
96.9%
1 1535
 
3.1%

ProfileJobTitle
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48761 
1
 
1083

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48761
97.8%
1 1083
 
2.2%

Length

2023-11-10T22:10:05.476557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:05.584888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48761
97.8%
1 1083
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 48761
97.8%
1 1083
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48761
97.8%
1 1083
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48761
97.8%
1 1083
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48761
97.8%
1 1083
 
2.2%

Login
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48375 
1
 
1469

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48375
97.1%
1 1469
 
2.9%

Length

2023-11-10T22:10:05.706605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:05.826435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48375
97.1%
1 1469
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 48375
97.1%
1 1469
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48375
97.1%
1 1469
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48375
97.1%
1 1469
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48375
97.1%
1 1469
 
2.9%

ProfileEmploymentLength
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
48825 
1
 
1019

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48825
98.0%
1 1019
 
2.0%

Length

2023-11-10T22:10:05.942821image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:06.054096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 48825
98.0%
1 1019
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 48825
98.0%
1 1019
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48825
98.0%
1 1019
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48825
98.0%
1 1019
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48825
98.0%
1 1019
 
2.0%

WebView
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
45075 
1
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45075
90.4%
1 4769
 
9.6%

Length

2023-11-10T22:10:06.170004image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:06.299819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 45075
90.4%
1 4769
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 45075
90.4%
1 4769
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45075
90.4%
1 4769
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45075
90.4%
1 4769
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45075
90.4%
1 4769
 
9.6%

SecurityModal
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
49137 
1
 
707

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49137
98.6%
1 707
 
1.4%

Length

2023-11-10T22:10:06.438279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:06.548766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49137
98.6%
1 707
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 49137
98.6%
1 707
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49137
98.6%
1 707
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49137
98.6%
1 707
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49137
98.6%
1 707
 
1.4%

ResendToken
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
49179 
1
 
665

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49179
98.7%
1 665
 
1.3%

Length

2023-11-10T22:10:06.666154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:06.774196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49179
98.7%
1 665
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 49179
98.7%
1 665
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49179
98.7%
1 665
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49179
98.7%
1 665
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49179
98.7%
1 665
 
1.3%

TransactionList
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
49195 
1
 
649

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49195
98.7%
1 649
 
1.3%

Length

2023-11-10T22:10:07.102706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:07.216895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49195
98.7%
1 649
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 49195
98.7%
1 649
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49195
98.7%
1 649
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49195
98.7%
1 649
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49195
98.7%
1 649
 
1.3%

NetworkFailure
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
49437 
1
 
407

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49437
99.2%
1 407
 
0.8%

Length

2023-11-10T22:10:07.341505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:07.451281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49437
99.2%
1 407
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 49437
99.2%
1 407
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49437
99.2%
1 407
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49437
99.2%
1 407
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49437
99.2%
1 407
 
0.8%

ListPicker
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
49475 
1
 
369

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 49475
99.3%
1 369
 
0.7%

Length

2023-11-10T22:10:07.582633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:07.691864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 49475
99.3%
1 369
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 49475
99.3%
1 369
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49475
99.3%
1 369
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49475
99.3%
1 369
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49475
99.3%
1 369
 
0.7%

OtherScreens
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1986197
Minimum0
Maximum30
Zeros774
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:10:07.809463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q38
95-th percentile13
Maximum30
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6559983
Coefficient of variation (CV)0.58980845
Kurtosis0.74559176
Mean6.1986197
Median Absolute Deviation (MAD)2
Skewness0.74472774
Sum308964
Variance13.366324
MonotonicityNot monotonic
2023-11-10T22:10:07.960052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5 5728
11.5%
4 5692
11.4%
3 4826
9.7%
6 4735
9.5%
7 4449
8.9%
8 4356
8.7%
2 4130
8.3%
9 3160
6.3%
1 2960
 
5.9%
10 2300
 
4.6%
Other values (20) 7508
15.1%
ValueCountFrequency (%)
0 774
 
1.6%
1 2960
5.9%
2 4130
8.3%
3 4826
9.7%
4 5692
11.4%
5 5728
11.5%
6 4735
9.5%
7 4449
8.9%
8 4356
8.7%
9 3160
6.3%
ValueCountFrequency (%)
30 2
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 9
 
< 0.1%
25 12
 
< 0.1%
24 13
 
< 0.1%
23 18
< 0.1%
22 20
< 0.1%
21 28
0.1%
20 37
0.1%

CreditCardScreens
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
0
45425 
2
 
1549
1
 
1519
3
 
1351

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 45425
91.1%
2 1549
 
3.1%
1 1519
 
3.0%
3 1351
 
2.7%

Length

2023-11-10T22:10:08.110715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:08.224714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 45425
91.1%
2 1549
 
3.1%
1 1519
 
3.0%
3 1351
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 45425
91.1%
2 1549
 
3.1%
1 1519
 
3.0%
3 1351
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 45425
91.1%
2 1549
 
3.1%
1 1519
 
3.0%
3 1351
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 45425
91.1%
2 1549
 
3.1%
1 1519
 
3.0%
3 1351
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 45425
91.1%
2 1549
 
3.1%
1 1519
 
3.0%
3 1351
 
2.7%

CreditMonitoringScreens
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92480539
Minimum0
Maximum5
Zeros26174
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:10:08.335303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.215996
Coefficient of variation (CV)1.3148669
Kurtosis0.23195136
Mean0.92480539
Median Absolute Deviation (MAD)0
Skewness1.1735329
Sum46096
Variance1.4786464
MonotonicityNot monotonic
2023-11-10T22:10:08.461373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 26174
52.5%
1 11250
22.6%
2 5097
 
10.2%
3 4642
 
9.3%
4 2679
 
5.4%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 26174
52.5%
1 11250
22.6%
2 5097
 
10.2%
3 4642
 
9.3%
4 2679
 
5.4%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 2679
 
5.4%
3 4642
 
9.3%
2 5097
 
10.2%
1 11250
22.6%
0 26174
52.5%

LoanScreens
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size389.5 KiB
1
26674 
0
17216 
2
5308 
3
 
646

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49844
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 26674
53.5%
0 17216
34.5%
2 5308
 
10.6%
3 646
 
1.3%

Length

2023-11-10T22:10:08.598099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-10T22:10:08.714078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 26674
53.5%
0 17216
34.5%
2 5308
 
10.6%
3 646
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 26674
53.5%
0 17216
34.5%
2 5308
 
10.6%
3 646
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26674
53.5%
0 17216
34.5%
2 5308
 
10.6%
3 646
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26674
53.5%
0 17216
34.5%
2 5308
 
10.6%
3 646
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26674
53.5%
0 17216
34.5%
2 5308
 
10.6%
3 646
 
1.3%

SavingsScreens
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35334243
Minimum0
Maximum10
Zeros45689
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size389.5 KiB
2023-11-10T22:10:08.835823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3782828
Coefficient of variation (CV)3.9007001
Kurtosis19.292238
Mean0.35334243
Median Absolute Deviation (MAD)0
Skewness4.3682141
Sum17612
Variance1.8996636
MonotonicityNot monotonic
2023-11-10T22:10:08.965519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 45689
91.7%
1 854
 
1.7%
4 658
 
1.3%
8 629
 
1.3%
3 623
 
1.2%
6 435
 
0.9%
5 370
 
0.7%
2 320
 
0.6%
7 173
 
0.3%
10 77
 
0.2%
ValueCountFrequency (%)
0 45689
91.7%
1 854
 
1.7%
2 320
 
0.6%
3 623
 
1.2%
4 658
 
1.3%
5 370
 
0.7%
6 435
 
0.9%
7 173
 
0.3%
8 629
 
1.3%
9 16
 
< 0.1%
ValueCountFrequency (%)
10 77
 
0.2%
9 16
 
< 0.1%
8 629
1.3%
7 173
 
0.3%
6 435
0.9%
5 370
0.7%
4 658
1.3%
3 623
1.2%
2 320
 
0.6%
1 854
1.7%

Interactions

2023-11-10T22:09:53.677780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.114936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.056154image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.974246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.840652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.899432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.786254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.710788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:53.803349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.243847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.177297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.089525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.096439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.016798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.908571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.840476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:53.922854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.362985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.294683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.200805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.230780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.130131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.026385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.960791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:54.037681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.471766image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.404990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.301063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.355745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.234788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.134049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:53.074760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:54.148091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.584344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.515873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.405108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.460681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.340961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.242627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:53.187073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:54.259548image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.697307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.625097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.508529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.567900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.450448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.352982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:53.302155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:54.380931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.820065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.746447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.621183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.682657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.566928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.470919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:53.439842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:54.512578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:47.937549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:48.860423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:49.730873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:50.790657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:51.677199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:52.586336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-10T22:09:53.565371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-10T22:10:09.171867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
userdayofweekhouragenumscreensOtherScreensCreditMonitoringScreensSavingsScreensminigameused_premium_featureenrolledlikedlocationInstitutionsVerifyPhoneBankVerificationVerifyDateOfBirthProfilePageVerifyCountryCycleidscreenSplashRewardsContainerEditProfileFinancesAlertsLeaderboardVerifyMobileVerifyHousingRewardDetailVerifyHousingAmountProfileMaritalStatusProfileEducationProfileEducationMajorRewardsAccountViewVerifyAnnualIncomeVerifyIncomeTypeProfileJobTitleLoginProfileEmploymentLengthWebViewSecurityModalResendTokenTransactionListNetworkFailureListPickerCreditCardScreensLoanScreens
user1.000-0.002-0.012-0.002-0.0020.0010.001-0.0020.0130.0000.0050.0090.0100.0140.0080.0000.0120.0080.0000.0000.0000.0000.0190.0000.0000.0000.0000.0000.0040.0000.0020.0030.0000.0000.0000.0000.0040.0000.0120.0000.0070.0000.0000.0060.0000.0000.0030.0040.007
dayofweek-0.0021.0000.014-0.0120.0070.0100.005-0.0040.0090.0180.0220.0100.0160.0070.0110.0140.0250.0000.0140.0170.0040.0280.0090.0080.0110.0110.0100.0190.0000.0010.0000.0000.0000.0000.0080.0180.0000.0090.0090.0080.0090.0090.0000.0000.0080.0000.0090.0020.023
hour-0.0120.0141.0000.014-0.082-0.075-0.041-0.0250.0050.0350.0630.0060.0890.0350.0660.0310.0940.0250.0380.0520.0480.0370.0290.0290.0130.0190.0220.0340.0250.0270.0260.0340.0240.0270.0000.0130.0230.0260.0250.0140.0250.0100.0110.0080.0070.0090.0110.0240.038
age-0.002-0.0120.0141.000-0.132-0.165-0.0480.0010.0440.0420.1550.0000.1310.0380.1460.0900.1300.0280.1250.1710.1770.0210.0280.0040.0520.0780.0140.0280.0210.0350.0210.0240.0220.0220.0220.0180.0190.0150.0200.0350.0200.0480.0230.0250.0240.0130.0300.0270.091
numscreens-0.0020.007-0.082-0.1321.0000.7220.4250.3050.0430.0800.3030.0110.4290.3190.4890.3570.3730.4260.4210.1450.1640.0730.2670.3370.1560.1920.1300.1430.3790.1850.3790.4260.4080.4200.1340.1470.2640.3230.3110.1060.3060.1880.0790.0840.0870.0270.0890.1780.229
OtherScreens0.0010.010-0.075-0.1650.7221.0000.0910.0770.0540.1200.3990.0000.4720.0880.5860.4470.4340.1100.6780.1000.2020.1030.0810.0940.1640.0590.0310.0760.0840.0460.0860.1220.1070.1130.0740.0420.0700.1020.1010.0890.0980.0670.0840.0980.0350.0250.0400.0320.349
CreditMonitoringScreens0.0010.005-0.041-0.0480.4250.0911.0000.1090.0610.0640.2540.0000.1480.4750.1750.0590.1400.1380.1540.0930.0660.0600.1810.0550.2500.1670.0930.3200.0800.1100.0790.0830.0790.0800.1180.1130.0520.0570.0530.0210.0500.0650.0140.0310.0640.0060.0380.1100.186
SavingsScreens-0.002-0.004-0.0250.0010.3050.0770.1091.0000.0330.0620.0410.0120.0330.1300.0560.0900.0260.4810.0680.0240.0310.0380.1570.3140.0140.1180.0660.0920.6790.0720.6870.6380.5700.5590.0400.0470.5170.3980.3810.0030.3880.0140.0040.0210.0460.0000.0280.0570.024
minigame0.0130.0090.0050.0440.0430.0540.0610.0331.0000.1090.0150.0110.0260.0240.0000.0690.0420.0190.0310.0380.0550.0540.0540.0000.0440.0190.0540.0350.0340.0460.0310.0280.0250.0240.0680.0170.0230.0200.0220.0290.0170.0040.0090.0160.0320.0080.0000.0080.040
used_premium_feature0.0000.0180.0350.0420.0800.1200.0640.0620.1091.0000.0480.0000.0740.0830.0490.2250.1010.0210.0170.0560.0020.0760.0270.0160.0370.0900.0240.0320.0000.0120.0000.0000.0010.0030.0320.1480.0010.0090.0000.0250.0000.0560.0210.0330.1060.0080.0000.0240.224
enrolled0.0050.0220.0630.1550.3030.3990.2540.0410.0150.0481.0000.0000.2980.0050.4400.2090.3190.0290.2530.0930.1910.0390.0080.0070.0480.1470.0130.1480.0400.0190.0420.0120.0190.0220.0340.0690.0430.0230.0220.0290.0160.0620.0330.0040.0490.0080.0190.0250.262
liked0.0090.0100.0060.0000.0110.0000.0000.0120.0110.0000.0001.0000.0040.0040.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0010.0000.0030.0000.0000.0000.0080.0000.0010.0000.0000.0000.0000.0040.0000.0000.0000.0000.0050.0020.006
location0.0100.0160.0890.1310.4290.4720.1480.0330.0260.0740.2980.0041.0000.0120.4900.1710.7450.0240.4710.0330.1100.0450.0000.0560.0350.0990.0170.1270.0410.0090.0440.0440.0420.0450.0190.0470.0290.0420.0430.0000.0380.0150.0270.0890.0330.0000.0000.0190.253
Institutions0.0140.0070.0350.0380.3190.0880.4750.1300.0240.0830.0050.0040.0121.0000.0370.1050.0420.1400.1130.0900.0440.1150.2190.0540.1850.1610.1030.1650.0810.0760.0800.0910.0830.0850.1010.1880.0440.0520.0470.0360.0470.0500.0240.0740.0670.0230.0220.1480.293
VerifyPhone0.0080.0110.0660.1460.4890.5860.1750.0560.0000.0490.4400.0000.4900.0371.0000.2890.4660.0460.6120.0790.1600.0390.0040.0700.0360.1350.0240.0770.0760.0170.0780.0840.0840.0890.0250.0740.0590.0750.0700.0000.0680.0000.0520.1030.0620.0090.0080.0170.276
BankVerification0.0000.0140.0310.0900.3570.4470.0590.0900.0690.2250.2090.0000.1710.1050.2891.0000.1360.0450.3390.0790.0770.0110.0000.0350.0000.0520.0000.0250.0050.0080.0030.0000.0000.0000.0000.0130.0080.0090.0000.0500.0000.0000.1710.0210.0040.0080.0130.0310.165
VerifyDateOfBirth0.0120.0250.0940.1300.3730.4340.1400.0260.0420.1010.3190.0050.7450.0420.4660.1361.0000.0000.3860.0270.1300.1070.0160.0370.0600.1220.0220.1340.0240.0120.0260.0340.0300.0340.0370.0640.0270.0370.0380.0140.0340.0340.0260.0990.0460.0000.0020.0230.238
ProfilePage0.0080.0000.0250.0280.4260.1100.1380.4810.0190.0210.0290.0000.0240.1400.0460.0450.0001.0000.0410.0000.0290.0160.2510.5030.0360.2480.0720.0470.4340.1280.4130.5030.4910.4670.1090.0780.2940.3880.3410.1010.3320.0600.0150.0180.0470.0000.0990.1950.110
VerifyCountry0.0000.0140.0380.1250.4210.6780.1540.0680.0310.0170.2530.0000.4710.1130.6120.3390.3860.0411.0000.0300.0880.0290.0710.0060.0350.1040.0420.0480.0440.0480.0380.0180.0350.0310.0430.0560.0500.0570.0350.0150.0360.0210.0680.1280.0380.0170.0180.0610.257
Cycle0.0000.0170.0520.1710.1450.1000.0930.0240.0380.0560.0930.0000.0330.0900.0790.0790.0270.0000.0301.0000.3340.1600.0390.0000.0340.0220.0220.0180.0000.0210.0000.0070.0000.0000.0000.0120.0060.0000.0000.0120.0010.0030.0000.0360.0080.0310.0100.0230.056
idscreen0.0000.0040.0480.1770.1640.2020.0660.0310.0550.0020.1910.0000.1100.0440.1600.0770.1300.0290.0880.3341.0000.0280.0170.0110.1200.0850.0030.0130.0000.0100.0000.0140.0060.0000.0700.0420.0100.0000.0050.0040.0000.0310.0110.0260.0270.0330.0120.0100.132
Splash0.0000.0280.0370.0210.0730.1030.0600.0380.0540.0760.0390.0000.0450.1150.0390.0110.1070.0160.0290.1600.0281.0000.0950.0000.1440.0730.0080.0840.0030.0320.0010.0070.0070.0000.0790.0470.0000.0040.0120.1170.0110.0430.0030.0580.0410.0170.0180.0070.160
RewardsContainer0.0190.0090.0290.0280.2670.0810.1810.1570.0540.0270.0080.0000.0000.2190.0040.0000.0160.2510.0710.0390.0170.0951.0000.1490.0860.1220.0970.0610.1580.4370.1480.1590.1690.1680.0470.0610.0890.1160.0980.0500.0900.0480.0000.0340.0600.0000.0430.1920.083
EditProfile0.0000.0080.0290.0040.3370.0940.0550.3140.0000.0160.0070.0000.0560.0540.0700.0350.0370.5030.0060.0000.0110.0000.1491.0000.0000.0960.0310.0280.3510.0670.3300.3970.4310.4060.0460.0170.2910.4750.4210.0680.4010.0250.0050.0080.0070.0000.0680.0890.024
Finances0.0000.0110.0130.0520.1560.1640.2500.0140.0440.0370.0480.0020.0350.1850.0360.0000.0600.0360.0350.0340.1200.1440.0860.0001.0000.0210.0370.0680.0090.0320.0070.0010.0030.0000.3130.0560.0090.0070.0000.0240.0000.0320.0270.0900.0160.0250.0270.0510.156
Alerts0.0000.0110.0190.0780.1920.0590.1670.1180.0190.0900.1470.0000.0990.1610.1350.0520.1220.2480.1040.0220.0850.0730.1220.0960.0211.0000.0890.0020.0890.0520.0850.1190.1160.1150.0410.1280.0480.0590.0540.0740.0570.1050.0000.0190.0940.0000.0550.1040.248
Leaderboard0.0000.0100.0220.0140.1300.0310.0930.0660.0540.0240.0130.0000.0170.1030.0240.0000.0220.0720.0420.0220.0030.0080.0970.0310.0370.0891.0000.0020.0610.0590.0610.0460.0510.0520.0260.0410.0380.0290.0230.0020.0220.0260.0000.0100.0410.0130.0190.0690.088
VerifyMobile0.0000.0190.0340.0280.1430.0760.3200.0920.0350.0320.1480.0000.1270.1650.0770.0250.1340.0470.0480.0180.0130.0840.0610.0280.0680.0020.0021.0000.0190.0170.0180.0530.0410.0380.0370.0060.0200.0460.0310.0000.0280.0160.0100.0270.0090.0120.0070.0630.072
VerifyHousing0.0040.0000.0250.0210.3790.0840.0800.6790.0340.0000.0400.0010.0410.0810.0760.0050.0240.4340.0440.0000.0000.0030.1580.3510.0090.0890.0610.0191.0000.0930.9560.5640.5910.5760.0600.0180.5220.4310.3760.0180.3720.0110.0020.0120.0140.0000.0310.0890.024
RewardDetail0.0000.0010.0270.0350.1850.0460.1100.0720.0460.0120.0190.0000.0090.0760.0170.0080.0120.1280.0480.0210.0100.0320.4370.0670.0320.0520.0590.0170.0931.0000.0870.0700.0740.0680.2100.0360.0390.0590.0400.0360.0300.0310.0000.0080.0420.0000.0290.1050.033
VerifyHousingAmount0.0020.0000.0260.0210.3790.0860.0790.6870.0310.0000.0420.0030.0440.0800.0780.0030.0260.4130.0380.0000.0000.0010.1480.3300.0070.0850.0610.0180.9560.0871.0000.5660.5750.5670.0580.0170.5170.4120.3670.0170.3640.0110.0000.0110.0150.0000.0280.0830.027
ProfileMaritalStatus0.0030.0000.0340.0240.4260.1220.0830.6380.0280.0000.0120.0000.0440.0910.0840.0000.0340.5030.0180.0070.0140.0070.1590.3970.0010.1190.0460.0530.5640.0700.5661.0000.6680.6620.0480.0270.3690.4720.4440.0040.4460.0080.0020.0170.0240.0000.0270.0970.034
ProfileEducation0.0000.0000.0240.0220.4080.1070.0790.5700.0250.0010.0190.0000.0420.0830.0840.0000.0300.4910.0350.0000.0060.0070.1690.4310.0030.1160.0510.0410.5910.0740.5750.6681.0000.9460.0500.0180.3990.5020.4630.0000.4470.0070.0070.0100.0300.0000.0320.0900.028
ProfileEducationMajor0.0000.0000.0270.0220.4200.1130.0800.5590.0240.0030.0220.0000.0450.0850.0890.0000.0340.4670.0310.0000.0000.0000.1680.4060.0000.1150.0520.0380.5760.0680.5670.6620.9461.0000.0470.0170.4010.4930.4630.0000.4480.0070.0050.0090.0330.0000.0330.0880.030
Rewards0.0000.0080.0000.0220.1340.0740.1180.0400.0680.0320.0340.0080.0190.1010.0250.0000.0370.1090.0430.0000.0700.0790.0470.0460.3130.0410.0260.0370.0600.2100.0580.0480.0500.0471.0000.0270.0420.0400.0320.0120.0290.0290.0110.0300.0390.0130.0540.1010.041
AccountView0.0000.0180.0130.0180.1470.0420.1130.0470.0170.1480.0690.0000.0470.1880.0740.0130.0640.0780.0560.0120.0420.0470.0610.0170.0560.1280.0410.0060.0180.0360.0170.0270.0180.0170.0271.0000.0010.0000.0040.0120.0060.0760.0130.0080.1280.0000.0280.0630.192
VerifyAnnualIncome0.0040.0000.0230.0190.2640.0700.0520.5170.0230.0010.0430.0010.0290.0440.0590.0080.0270.2940.0500.0060.0100.0000.0890.2910.0090.0480.0380.0200.5220.0390.5170.3690.3990.4010.0420.0011.0000.5920.4830.0000.4610.0000.0000.0120.0000.0000.0290.0570.005
VerifyIncomeType0.0000.0090.0260.0150.3230.1020.0570.3980.0200.0090.0230.0000.0420.0520.0750.0090.0370.3880.0570.0000.0000.0040.1160.4750.0070.0590.0290.0460.4310.0590.4120.4720.5020.4930.0400.0000.5921.0000.7230.0050.6890.0000.0090.0140.0000.0000.0380.0640.011
ProfileJobTitle0.0120.0090.0250.0200.3110.1010.0530.3810.0220.0000.0220.0000.0430.0470.0700.0000.0380.3410.0350.0000.0050.0120.0980.4210.0000.0540.0230.0310.3760.0400.3670.4440.4630.4630.0320.0040.4830.7231.0000.0000.8560.0000.0080.0110.0000.0000.0240.0560.021
Login0.0000.0080.0140.0350.1060.0890.0210.0030.0290.0250.0290.0000.0000.0360.0000.0500.0140.1010.0150.0120.0040.1170.0500.0680.0240.0740.0020.0000.0180.0360.0170.0040.0000.0000.0120.0120.0000.0050.0001.0000.0000.0280.0070.0040.0000.0360.0450.0180.035
ProfileEmploymentLength0.0070.0090.0250.0200.3060.0980.0500.3880.0170.0000.0160.0000.0380.0470.0680.0000.0340.3320.0360.0010.0000.0110.0900.4010.0000.0570.0220.0280.3720.0300.3640.4460.4470.4480.0290.0060.4610.6890.8560.0001.0000.0000.0080.0130.0000.0000.0210.0560.019
WebView0.0000.0090.0100.0480.1880.0670.0650.0140.0040.0560.0620.0040.0150.0500.0000.0000.0340.0600.0210.0030.0310.0430.0480.0250.0320.1050.0260.0160.0110.0310.0110.0080.0070.0070.0290.0760.0000.0000.0000.0280.0001.0000.0020.0030.0570.0000.0680.0890.371
SecurityModal0.0000.0000.0110.0230.0790.0840.0140.0040.0090.0210.0330.0000.0270.0240.0520.1710.0260.0150.0680.0000.0110.0030.0000.0050.0270.0000.0000.0100.0020.0000.0000.0020.0070.0050.0110.0130.0000.0090.0080.0070.0080.0021.0000.0120.0050.0000.0000.0000.025
ResendToken0.0060.0000.0080.0250.0840.0980.0310.0210.0160.0330.0040.0000.0890.0740.1030.0210.0990.0180.1280.0360.0260.0580.0340.0080.0900.0190.0100.0270.0120.0080.0110.0170.0100.0090.0300.0080.0120.0140.0110.0040.0130.0030.0121.0000.0100.0080.0000.0200.039
TransactionList0.0000.0080.0070.0240.0870.0350.0640.0460.0320.1060.0490.0000.0330.0670.0620.0040.0460.0470.0380.0080.0270.0410.0600.0070.0160.0940.0410.0090.0140.0420.0150.0240.0300.0330.0390.1280.0000.0000.0000.0000.0000.0570.0050.0101.0000.0000.0150.0450.122
NetworkFailure0.0000.0000.0090.0130.0270.0250.0060.0000.0080.0080.0080.0000.0000.0230.0090.0080.0000.0000.0170.0310.0330.0170.0000.0000.0250.0000.0130.0120.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0360.0000.0000.0000.0080.0001.0000.0000.0000.002
ListPicker0.0030.0090.0110.0300.0890.0400.0380.0280.0000.0000.0190.0050.0000.0220.0080.0130.0020.0990.0180.0100.0120.0180.0430.0680.0270.0550.0190.0070.0310.0290.0280.0270.0320.0330.0540.0280.0290.0380.0240.0450.0210.0680.0000.0000.0150.0001.0000.0570.043
CreditCardScreens0.0040.0020.0240.0270.1780.0320.1100.0570.0080.0240.0250.0020.0190.1480.0170.0310.0230.1950.0610.0230.0100.0070.1920.0890.0510.1040.0690.0630.0890.1050.0830.0970.0900.0880.1010.0630.0570.0640.0560.0180.0560.0890.0000.0200.0450.0000.0571.0000.070
LoanScreens0.0070.0230.0380.0910.2290.3490.1860.0240.0400.2240.2620.0060.2530.2930.2760.1650.2380.1100.2570.0560.1320.1600.0830.0240.1560.2480.0880.0720.0240.0330.0270.0340.0280.0300.0410.1920.0050.0110.0210.0350.0190.3710.0250.0390.1220.0020.0430.0701.000

Missing values

2023-11-10T22:09:54.770864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-10T22:09:55.672159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

userdayofweekhouragenumscreensminigameused_premium_featureenrolledlikedlocationInstitutionsVerifyPhoneBankVerificationVerifyDateOfBirthProfilePageVerifyCountryCycleidscreenSplashRewardsContainerEditProfileFinancesAlertsLeaderboardVerifyMobileVerifyHousingRewardDetailVerifyHousingAmountProfileMaritalStatusProfileEducationProfileEducationMajorRewardsAccountViewVerifyAnnualIncomeVerifyIncomeTypeProfileJobTitleLoginProfileEmploymentLengthWebViewSecurityModalResendTokenTransactionListNetworkFailureListPickerOtherScreensCreditCardScreensCreditMonitoringScreensLoanScreensSavingsScreens
023513632231500000010100110000000000000000001000000070010
133358861241300001110101000000000000000000000000000050010
225441411923301010000000101000000000000000000000000000010
3234192416284000101010100000001010010000100000000000060310
4515491183132001101110011100000000000000000000000000100210
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